The accurate monitoring of a battery system state of charge (SOC) is a common issue in battery applications. There are four main methods for monitoring SOC. One method is to determine SOC by dividing the charge left in battery system divided by the full charge capacity of the battery system. However, it is generally non-trivial to measure the charge left in the battery system directly, especially in a dynamic environment. An alternate form of this first method is to calculate the integral of current over time as the charge in the battery system changes. However, in such a method, the current sensor tolerance is generally limited and errors will typically accumulate. Further, the initial SOC, full charge capacity, and the battery efficiencies during charge and discharge cycles are difficult to obtain. Additionally, using such a method becomes extremely difficult in circumstances where charging and discharging occur randomly, as in hybrid electric vehicles, or where there are no clear charge/discharge cycles.
A second method is to use the relation of electric motive force (EMF) and SOC of a battery system. That is, using EMF to decide SOC, since EMF and SOC have a monotonic relationship in many types of battery systems. In particular, this method relies on using the open circuit voltage (OCV) as the EMF. However, due to the capacitance in a battery, it can take several hours of relaxing (i.e., zero current draw) to let OCV approach EMF, rendering such a method unsuitable for real-time monitoring.
A third method is to use the first method to estimate SOC and use the second method to adjust the estimate of SOC. However, since such a method still relies on OCV, it is generally difficult to provide a period of operational time with zero current to provide an accurate measure of EMF, resulting in inaccurate measurement of SOC.
The fourth method is Extended Kalman Filter Method. This method requires an extensive calculation on micro controller, which is a bottle neck for small micro controller chips.